The healthcare industry is undergoing a major transition worldwide in response to economic and regulatory pressures. Industry transformation and rapid advancement in technology are creating tremendous amounts of electronic text and multimedia information. Managing and querying these collections is a formidable technical challenge, This Small Business Innovative Research Phase I project proposes to develop a novel Bayesian integrated text and multimedia retrieval engine for health data networks. The BIR methodology adopts a full Bayesian approach to information retrieval by defining prior distributions on the term probabilities of relevance. It also makes use of a database of past user queries and relevance feedback to coherently assess relevance for a new query. Relevance feedback is performed by updating the prior distribution to reflect newly acquired user relevance judgements. Unlike other information retrieval engines which do not require extensive human interaction, the BIR model incorporates user's feedback, and can be tailored to meet the information needs of a diversified audience. The applicants propose to integrate text and image based queries using the BIR methodology. Feature vectors extracted from images are transformed into feature terms and then combined with text terms in a common query framework.